Abstract

A random Boolean network (RBN) may be controlled through the use of a learning classifier system (LCS) – an eXtended Classifier System (XCS) can evolve a rule set that directs an RBN from any state to a target state. However, the rules evolved may not be optimal, in terms of minimising the total cost of the paths used to direct the network from any state to a specified attractor. Here we uncover the optimal set of control rules via an exhaustive algorithm. The performance of an LCS (XCS) on the RBN control problem is assessed in light of the newly uncovered optimal rule set.

This research was partly funded by the Department for Transport, via Innovate UK and the AIR round 4 programme, under the Onward Journey Planning Assistant (OJPA) project and partly funded by EIT Digital under the Real–Time Flow project, activity 18387–SGA2018.